David Aanensen from the University of Oxford and the Wellcome Sanger Institute in the United Kingdom and Maria van Kerkhove of the World Health Organization in Switzerland will combine next generation DNA sequencing technology with a simple, web-based data collection, processing, and distribution platform to better track the global spread of deadly infectious diseases including Middle East Respiratory Syndrome (MERS-CoV). MERS - also known as camel flu - is a viral disease that causes fever, cough, diarrhea, and shortness of breath, and is transmitted from camels to humans. One third of people diagnosed with the disease die. Next generation sequencing (NGS) technology allows rapid, inexpensive detection of pathogens as they spread. However, laboratories in different member states use different formats for sequencing data, and there is no mechanism for sharing it in real time. This limits the value of the technology for stopping outbreaks. To address this, they will establish routine sequencing protocols for both human and camel samples, and develop an interactive web platform on which the sequencing and epidemiological data can be shared. This will help develop more effective, real-time medical and non-medical interventions at local, national, and international levels. Once established, the protocols developed here may be applied to outbreaks of other diseases.

Achim Hoerauf of IMMP in Germany will apply artificial intelligence (AI) to speed the development of treatments for onchocerciasis, which is an infectious disease commonly known as River Blindness caused by a parasitic worm. The parasites are spread by affected blackflies, and the worm larvae accumulate in the skin and eyes, causing irritation and sometimes blindness. Nearly 21 million cases occur each year, and 99% of affected people live in Africa. The drug currently used for treatment kills only worm larvae, and studies are ongoing to identify more effective drugs that target adult worms. However, evaluating these drug candidates requires manual analysis using microscopy of samples of irritated skin from patients after treatment. This process is time consuming and slows drug development. To address this, they will use samples that have already been manually annotated to train an AI system to automatically analyze future samples to recognize worm body parts, gender, vitality, and stage of development. Once established, the AI system will be tested with samples from a new clinical trial - tissues from patients treated with the new drug will be analyzed in parallel by human and computer. Once optimized, the AI system will take over the analysis, and the much slower human analysis will only be needed as a quality control system.